Improving Music Recommendation Using Distributed Representation

Wang, D; Deng, S; Liu, S; Xu, G

Improving Music Recommendation Using Distributed Representation

Wang, D Deng, S Liu, S Xu, G

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 25th International Conference Companion on World Wide Web, 2016, pp. 125 - 126 (2)
Issue Date:: 2016-04-11

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Field	Value	Language
dc.contributor.author	Wang, D	en_US
dc.contributor.author	Deng, S	en_US
dc.contributor.author	Liu, S	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.date	2016-04-11	en_US
dc.date.issued	2016-04-11	en_US
dc.identifier.citation	Proceedings of the 25th International Conference Companion on World Wide Web, 2016, pp. 125 - 126 (2)	en_US
dc.identifier.isbn	978-1-4503-4144-8	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43956
dc.description.abstract	In this paper, a music recommendation approach based on distributed representation is presented. The proposed approach firstly learns the distributed representations of music pieces and acquires users' preferences from listening records. Then, it recommends appropriate music pieces whose distributed representations are in accordance with target users' preferences. Experiments on a real world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods.	en_US
dc.publisher	ACM	en_US
dc.relation.ispartof	Proceedings of the 25th International Conference Companion on World Wide Web	en_US
dc.relation.ispartof	World Wide Web Conference	en_US
dc.relation.isbasedon	10.1145/2872518.2889399	en_US
dc.rights	© ACM 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 25th International Conference Companion on World Wide Web, 2016, pp. 125 - 126 (2) http://doi.acm.org/10.1145/2872518.2889399	en_US
dc.title	Improving Music Recommendation Using Distributed Representation	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Montreal, Canada	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access
pubs.consider-herdc	false	en_US
pubs.finish-date	2016-04-15	en_US
pubs.place-of-publication	USA	en_US
pubs.publication-status	Published	en_US
pubs.start-date	2016-04-11	en_US

Abstract:

In this paper, a music recommendation approach based on distributed representation is presented. The proposed approach firstly learns the distributed representations of music pieces and acquires users' preferences from listening records. Then, it recommends appropriate music pieces whose distributed representations are in accordance with target users' preferences. Experiments on a real world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods.

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http://hdl.handle.net/10453/43956